The Profitability Of Technical Trading Rules: A Combined Signal Approach
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Bibliographic record
Abstract
<p class="MsoNormal" style="text-align: justify; margin: 0in 34.2pt 0pt 0.5in; mso-layout-grid-align: none;"><span style="font-family: Times New Roman;"><span style="font-size: 10pt; mso-bidi-font-style: italic;" lang="EN-CA">The focus of this paper is to determine the profitability of technical trading rules by evaluating their ability to outperform the na&iuml;ve buy-and-hold trading strategy. Moving average cross-over rules, filter rules, Bollinger Bands, and trading range break-out rules are tested on the </span><span style="font-size: 10pt;" lang="EN-CA">S&amp;P/TSX 300 Index, the Dow Jones Industrial Average Index, NASDAQ Composite Index, and the Canada/U.S. spot exchange rate<span style="mso-bidi-font-style: italic;">. After accounting for transaction costs, excess returns are generated by the moving average cross-over rules and trading range break-out rules for the </span>S&amp;P/TSX 300 Index<span style="mso-bidi-font-style: italic;">, </span>NASDAQ Composite Index and the Canada/U.S. spot exchange rate<span style="mso-bidi-font-style: italic;">. Filter rules also earn excess returns when applied on the Canada/U.S. spot exchange rate. The bootstrap methodology is used to determine the statistical significance of the results. The profitability of the technical trading rules is further enhanced with a combined signal approach.</span></span></span></p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it